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DMA_PPT_Analytics FINAL Sept 2017

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DMA Analytics Community Webinar Sept 13, 2017

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DMA_PPT_Analytics FINAL Sept 2017

  1. 1. DMA Analytics Community Monthly Webinar Series: Update on Ad Fraud: How Bots are Skewing Your Analytics and ROI Led by Dr. Augustine Fou
  2. 2. Connect  Leadership Council is made up of members like you.  Monthly webinars on advanced marketing analytics.  Regional Roundtables: Sept 13/Denver; Sept 20/Boston  Publishes an annual Analytics Journal  Call for 2018 Journal Advertisers  Sponsors an Analytic Challenge  DMA360 Analytics Community Channel  More at THEDMA.org/acc
  3. 3. Update on Ad Fraud First , The Good News September 2017 Augustine Fou, PhD. acfou [at] mktsci.com 212. 203 .7239
  4. 4. “there is light at the end of the tunnel … as long as we stay vigilant, work hard, and dispel assumptions.”
  5. 5. September 2017 / Page 5marketing.scienceconsulting group, inc. linkedin.com/in/augustinefou Some marketers stopped buying poop water “1) start with ‘poop water’ and filter it before you drink it?, or 2) start with fresh water?” Good publishers are “fresh water.”
  6. 6. September 2017 / Page 6marketing.scienceconsulting group, inc. linkedin.com/in/augustinefou P&G: slash $140M, no impact “Procter & Gamble's concerns about where its ads were showing up online contributed to a $140 million cutback in the company's digital ad spending last quarter, the company said Thursday. That helped the world's biggest advertiser beat earnings expectations. Perhaps even more noteworthy, however, organic sales outperformed both analyst forecasts and key rivals at 2% growth despite the drop in ad support. Source: AdAge, July 2017
  7. 7. September 2017 / Page 7marketing.scienceconsulting group, inc. linkedin.com/in/augustinefou Chase: cut 99% reach, no impact “JPMorgan had already decided last year to oversee its own programmatic buying operation. Advertisements for JPMorgan Chase were appearing on about 400,000 websites a month. [But] only 12,000, or 3 percent, led to activity beyond an impression. [Then, Chase] limited its display ads to about 5,000 websites. We haven’t seen any deterioration on our performance metrics,” Ms. Lemkau said.” “99% reduction in ‘reach’ … Same Results.” Source: NYTimes, March 29, 2017 (because it wasn’t real, human reach)
  8. 8. How do we solve fraud?
  9. 9. September 2017 / Page 9marketing.scienceconsulting group, inc. linkedin.com/in/augustinefou Bifurcate good pubs from “other” 100% bot traffic “fake (cash out) sites” • No content • Stolen content • Fake content “sites with real content that real humans want to read” Source: DCN/ WhiteOps 2015 “sites you’ve heard of” WSJ ESPN NYTimes Economist Reuters Elle Good Publishers (good business practices) “sites that carry ads”
  10. 10. September 2017 / Page 10marketing.scienceconsulting group, inc. linkedin.com/in/augustinefou Fraud diverts ad spend to fraudsters Good Publishers “sites that carry ads” • No content • Few humans • Low CPMS $40 Search Spend Display Spend $40 $21$30 $3 Google Search FB+Google Display $29 (outside Google/Facebook) $83 Digital Spend Source: eMarketer March 2017 47% programmati c
  11. 11. September 2017 / Page 11marketing.scienceconsulting group, inc. linkedin.com/in/augustinefou $29 (outside Google/Facebook) There’s 160X more “sites with ads” Good Publishers “sites with ads” Source: Verisign, Q4 2016 329M domains est. 164 million “sites that carry ads” “sites you’ve heard of” WSJ ESPN NYTimes Economist Reuters Elle 3% no ads carry ads 160X more 47% programmati c est. 1 million
  12. 12. September 2017 / Page 12marketing.scienceconsulting group, inc. linkedin.com/in/augustinefou 700X more There’s 700X more fake apps 7M apps Source: Statista, March 2017 6.99 million 96% “apps that carry ads” 10,000 “apps you’ve heard of” Facebook Spotify Pandora Zynga Pokemon YouTube $29 (outside Google/Facebook) 47% programmati c Facebook, 2015 Users use 8 – 15 apps on their phones. Spotify, 2016 People have 25 apps on their phones, use 5-8 regularly Forrester Research, May 2017 Humans “use 9 apps per day, 30 per month”
  13. 13. September 2017 / Page 13marketing.scienceconsulting group, inc. linkedin.com/in/augustinefou Examples of fake sites, fake apps Fake Sites (10s of millions) Source: Sadbottrue.com Fake Apps (millions)
  14. 14. September 2017 / Page 14marketing.scienceconsulting group, inc. linkedin.com/in/augustinefou Fake sites successfully sell ads… how? 100% viewability (but, it’s fake) AD Stack ads all above the fold to trick detection 0% NHT (but, it’s fake) Buy traffic that is guaranteed to pass fraud filters clean placement (but, it’s fake) Pass fake source to trick reports of placement details http://www.olay.c om/skin-care- products/OlayPro- X?utm_source=elle &utm_medium=di splay + + “by tricking measurement and reporting”
  15. 15. September 2017 / Page 15marketing.scienceconsulting group, inc. linkedin.com/in/augustinefou Current detection cannot catch it In-Ad (billions of ads) • Limitations – tag is in foreign iframe, cannot look outside itself ad tag / pixel (in-ad measurement) In-Network (trillions of bids) On-Site (millions of pageviews) javascript embed (on-site measurement) • Limitations – most detailed analysis of visitors, bots still get by • Limitations – relies on blacklists or probabilistic algorithms, least info ad served bot human fraud site good site
  16. 16. September 2017 / Page 16marketing.scienceconsulting group, inc. linkedin.com/in/augustinefou Plainly incorrect measurements Incorrect IVT Measurement Sources 1 and 2 measured on-page Source 3 in foreign iframe 1x1 pixel incorrectly reported as 100% viewable Incorrect Viewability
  17. 17. September 2017 / Page 17marketing.scienceconsulting group, inc. linkedin.com/in/augustinefou Tag placement yields opposite results Tag (in foreign iframe) Tag (on page) window sizes detected as 0x0 or 0x8 pixels correct window sizes for ads detected 0% humans 60% bots 60% humans 3% bots “fraud measurements could be entirely wrong, depending on where the tag is placed and where the measurement is done.”
  18. 18. … if you don’t have 100% measurement and very detailed reports.
  19. 19. You’re NOT getting what you paid for…
  20. 20. September 2017 / Page 20marketing.scienceconsulting group, inc. linkedin.com/in/augustinefou Thought you bought ESPN? Nope publisherA.com ALL fake inventory because, PublisherA does NOT sell any ads on any exchanges! “Fake sites must pretend to be mainstream ones in order to sell inventory.”
  21. 21. September 2017 / Page 21marketing.scienceconsulting group, inc. linkedin.com/in/augustinefou Thought you bought reach? Nope $1 CPM Top 10 sites = 66% of imps $5 CPM Top 10 sites = 74% of imps $0.50 CPM Top 5 sites = 100% of imps $10 CPM Top 10 sites = 71% of imps Majority of your ads ran on 5-10 sites/apps
  22. 22. September 2017 / Page 22marketing.scienceconsulting group, inc. linkedin.com/in/augustinefou Thought your ads ran during waking hours? Most of budget wasted between 12a – 4a; to bots 98% impressions blown in 1st hour (12a-1a) HOURLY CHART
  23. 23. September 2017 / Page 23marketing.scienceconsulting group, inc. linkedin.com/in/augustinefou Thought your ads were geotargeted? Not Normal – in both campaigns 1. 100% mobile apps; 100% Android; same top 15 apps in both markets 2. 100% of impressions generated between 4a – 5a local time 3. 100% fake devices; 15 unique devices generated top 95% impressions 4. 100% data center traffic, randomized through residential proxies
  24. 24. September 2017 / Page 24marketing.scienceconsulting group, inc. linkedin.com/in/augustinefou Thought fraud filters reduced fraud? Nope 1. Fraud filters are no better than manual blacklists 2. In some cases it’s worse when filter is on 3. Using fraud filters adds 20 – 24% to costs; manual blacklists are free
  25. 25. September 2017 / Page 25marketing.scienceconsulting group, inc. linkedin.com/in/augustinefou End of month traffic/impressions fulfillment Caused by bots Caused by humans A B
  26. 26. September 2017 / Page 26marketing.scienceconsulting group, inc. linkedin.com/in/augustinefou Paid for mobile, 3 bad apps ate most of budget com.jiubang com.flashlight com.latininput 3 bad apps on fake devices at 75% of your budget
  27. 27. September 2017 / Page 27marketing.scienceconsulting group, inc. linkedin.com/in/augustinefou 1 billion mobile display ads – 43% overall fraud 66% avg fraud 18% avg fraud 1. 9% of the apps (blue dots) caused 52% of total impressions, 80% of fraud impr. 2. 91% of apps caused 48% of the total impressions, 20% of fraud impressions 3.Overall average – 43% of impressions were fraudulent • 1 billion mobile display impressions • Nearly 1,000 apps cross referenced with SDK 1 (52% of imps) 2 (48% of imps)
  28. 28. September 2017 / Page 28marketing.scienceconsulting group, inc. linkedin.com/in/augustinefou Humans (dark blue) vs Bots (dark red) Good Publishers Ad Networks Open Exchange 75% 2% 17% 30% 3% 72%
  29. 29. September 2017 / Page 29marketing.scienceconsulting group, inc. linkedin.com/in/augustinefou Directly measured viewability, by type “Taking viewability as 50% of the pixels in view or greater, we can see statistically different rates of viewability by network.” Good Publishers Ad Networks Open Exchange 91% viewable 66% viewable 41% viewable
  30. 30. September 2017 / Page 30marketing.scienceconsulting group, inc. linkedin.com/in/augustinefou Not Productive = Naked, Bots, Unviewable Naked ad calls + Not viewable + Confirmed bots = Not productive Ad Networks Open Exchanges 47% avg 77% avg 11% avg Good Publishers Naked ad calls Naked ad calls
  31. 31. September 2017 / Page 31marketing.scienceconsulting group, inc. linkedin.com/in/augustinefou Thought your $1 was “working media”? Nope “When buying programmatic exchanges, only 57 – 63 cents of every $1 spent goes towards working digital media.” “mark up” “working media” “working media” “mark up”
  32. 32. September 2017 / Page 32marketing.scienceconsulting group, inc. linkedin.com/in/augustinefou Corroborated by ANA, WFA Studies Source: ANA, May 2017 Source: WFA, April 2017
  33. 33. September 2017 / Page 33marketing.scienceconsulting group, inc. linkedin.com/in/augustinefou Digital Ad Productivity – for every $1 spent Good Publishers Ad Networks Open Exchange 91% viewable 40% fees 40% fees 30% NHT 70% NHT No fees 3% NHT 97% Not NHT 70% Not NHT 30% Not NHT 66% viewable 41% viewable 75% confirmed human 17%confirmed human 3% confirmed human 68¢ 7¢ 1¢ “human viewable ads” “human viewable ads” “human viewable ads” “not working media” “not working media”
  34. 34. September 2017 / Page 34marketing.scienceconsulting group, inc. linkedin.com/in/augustinefou Bots Mess Up Your Analytics
  35. 35. September 2017 / Page 35marketing.scienceconsulting group, inc. linkedin.com/in/augustinefou Conversion rates artificially depressed 7% conversion rate 13% conversion rate artificially low actually correct
  36. 36. September 2017 / Page 36marketing.scienceconsulting group, inc. linkedin.com/in/augustinefou Quantity metrics are easily “tuned” click on links load webpages tune bounce rate tune pages/visit “bad guys’ bots are advanced enough to fake most metrics”
  37. 37. September 2017 / Page 37marketing.scienceconsulting group, inc. linkedin.com/in/augustinefou Conversion metrics easily “hacked” Programmatic display (18-45% clicks from advanced bots) Premium publishers (0% clicks from bots) 0.13% CTR (18% of clicks by bots) 1.32% CTR (23% of clicks by bots) 5.93% CTR (45% of clicks by bots) Campaign KPI: CTRs
  38. 38. September 2017 / Page 38marketing.scienceconsulting group, inc. linkedin.com/in/augustinefou Fake clicks mess up CTRs, hide in averages Line item details Overall average 9.4% CTR “fraud hides easily in averages”
  39. 39. September 2017 / Page 39marketing.scienceconsulting group, inc. linkedin.com/in/augustinefou Fake demographic information
  40. 40. September 2017 / Page 40marketing.scienceconsulting group, inc. linkedin.com/in/augustinefou Suspicious web and mobile placements .xyz domains suspicious mobile apps
  41. 41. September 2017 / Page 41marketing.scienceconsulting group, inc. linkedin.com/in/augustinefou Bots easily trick AI/ML algorithms “Humans are hard to predict … … but bots give you beautiful signals.” Source: Claudia Perlich, PhD. Data Scientist, Dstilllery
  42. 42. What Savvy Data-Driven Marketers do …
  43. 43. Do your OWN experiments ..
  44. 44. September 2017 / Page 44marketing.scienceconsulting group, inc. linkedin.com/in/augustinefou Do a digital media health check Marketer 1 • Blue means humans • Red means bots Marketer 2 “what is the quality of traffic arriving on your site from various sources – organic and paid?”
  45. 45. September 2017 / Page 45marketing.scienceconsulting group, inc. linkedin.com/in/augustinefou Actively review and scrub campaigns Launch Week 3 and beyondWeek 2 Initial baseline measurement Measurement after first optimization After eliminating several “problematic” networks
  46. 46. September 2017 / Page 46marketing.scienceconsulting group, inc. linkedin.com/in/augustinefou Shift budgets to quality (high human) Lower quality paid sources mean higher cost per human acquired – like 11X the cost. Sources of different quality send widely different amounts of humans to landing pages. “mitigation doesn’t even require technology!”
  47. 47. September 2017 / Page 47marketing.scienceconsulting group, inc. linkedin.com/in/augustinefou Optimize for real human conversions Organic sources have more humans (dark blue) Conversion actions (calls) are well correlated to humans; bots don’t call
  48. 48. September 2017 / Page 48marketing.scienceconsulting group, inc. linkedin.com/in/augustinefou Measure every point of the funnel Measure Ads Measure Arrivals Measure Conversions 346 1743 5 156 A B 30X more human conversion events • More arrivals • Better quality more humans (blue) good publishers low-cost media, ad exchanges
  49. 49. Buy from Good Publishers
  50. 50. September 2017 / Page 50marketing.scienceconsulting group, inc. linkedin.com/in/augustinefou Good publishers act to reduce bots Publisher 1 – stopped buying traffic Publisher 2 – filtered data center traffic
  51. 51. September 2017 / Page 51marketing.scienceconsulting group, inc. linkedin.com/in/augustinefou Good publishers protect advertisers On-Site measurement, bots are still coming In-Ad measurement, bots and data centers filtered 11% red -9% (filtered GIVT and data centers) 2% red “Filter data center traffic and not call the ads”
  52. 52. September 2017 / Page 52marketing.scienceconsulting group, inc. linkedin.com/in/augustinefou Good publishers protect their users 42 trackers 24.3s load time 8 trackers 1.3s load time “minimize 3rd party javascript trackers on pages”
  53. 53. September 2017 / Page 53marketing.scienceconsulting group, inc. linkedin.com/in/augustinefou Good publishers have good practices “good business practices lead to good looking data” Good Publishers “sites that carry ads” • source traffic • audience extension • auto-refresh • traffic laundering • don‘t source traffic • protect advertisers • protect consumers
  54. 54. September 2017 / Page 54marketing.scienceconsulting group, inc. linkedin.com/in/augustinefou How can we tell “good” from “other?” “Business practice review by independent 3rd party provides the trust and assurance that distinguishes good publishers from ‘sites that carry ads’.”
  55. 55. September 2017 / Page 55marketing.scienceconsulting group, inc. linkedin.com/in/augustinefou Savvy data-driven marketers … 1. Stay vigilant • Check your own analytics for anything suspicious 2. Work hard • Actively scrub your own campaigns when you see something suspicious 3. Dispel assumptions • Don’t’ assume data-driven marketers are immune to ad fraud; know how the analytics can be messed up and run your own experiments to prove value.
  56. 56. September 2017 / Page 56marketing.scienceconsulting group, inc. linkedin.com/in/augustinefou About the Author September 2017 Augustine Fou, PhD. acfou [@] mktsci.com 212. 203 .7239
  57. 57. September 2017 / Page 57marketing.scienceconsulting group, inc. linkedin.com/in/augustinefou Dr. Augustine Fou – Independent Ad Fraud Researcher 2013 2014 Follow me on LinkedIn (click) and on Twitter @acfou (click) Further reading: http://www.slideshare.net/augustinefou/presentation s https://www.linkedin.com/today/author/augustinefo u 2016 2015
  58. 58. September 2017 / Page 58marketing.scienceconsulting group, inc. linkedin.com/in/augustinefou Harvard Business Review Excerpt: Hunting the Bots Fou, a prodigy who earned a Ph.D. from MIT at 23, belongs to the generation that witnessed the rise of digital marketers, having crafted his trade at American Express, one of the most successful American consumer brands, and at Omnicom, one of the largest global advertising agencies. Eventually stepping away from corporate life, Fou started his own practice, focusing on digital marketing fraud investigation. Fou’s experiment proved that fake traffic is unproductive traffic. The fake visitors inflated the traffic statistics but contributed nothing to conversions, which stayed steady even after the traffic plummeted (bottom chart). Fake traffic is generated by “bad-guy bots.” A bot is computer code that runs automated tasks.
  59. 59. Connect QUESTIONS? Use the “Questions” feature on the control panel. Many thanks to our speaker for sharing his knowledge with us today. Dr. Augustine Fou augustine.fou3@gmail.com Call for presenters for 2018! Do you have an interesting case study or application- based story to share? We have sponsorship opportunities, too. Contact us at DMACommunities@thedma.org
  60. 60. Upcoming Events:  Oct 11, 1pm ET: Real-Time Decisions for Improving the Customer Experience  Nov 9, 1pm ET: Comparing SAS/R/SPSS/Python for Data Science Visit us anytime: THEDMA.ORG | DMA360 Thank you for attending today. Be sure to give us your feedback via the survey coming to you shortly.

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